{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:17:01Z","timestamp":1783127821964,"version":"3.54.6"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100020725","name":"Hubei Key Laboratory of Intelligent Geo-Information Processing","doi-asserted-by":"publisher","award":["KLIGIP-2023-B02"],"award-info":[{"award-number":["KLIGIP-2023-B02"]}],"id":[{"id":"10.13039\/100020725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116087","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:10:06Z","timestamp":1777569006000},"page":"116087","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SAformer: A time series anomaly detection model based on Similarity-Aware attention"],"prefix":"10.1016","volume":"344","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5821-3814","authenticated-orcid":false,"given":"Yunkai","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9639-5139","authenticated-orcid":false,"given":"Guiling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongda","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116087_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113690","article-title":"An enhanced CLKAN-RF framework for robust anomaly detection in unmanned aerial vehicle sensor data","volume":"319","author":"Li","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116087_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110215","article-title":"Retentive network-based time series anomaly detection in cyber-physical systems","volume":"145","author":"Min","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116087_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113767","article-title":"Anomaly detection in online credit card data using optimized multi-view heterogeneous graph neural networks","volume":"324","author":"John Berkmans","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116087_b4","first-page":"1","article-title":"Large-area land-cover changes monitoring with time-series remote sensing images using transferable deep models","volume":"60","author":"Yan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116087_b5","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3033","article-title":"DCdetector: Dual attention contrastive representation learning for time series anomaly detection","author":"Yang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116087_b6","unstructured":"J. Xu, H. Wu, J. Wang, M. Long, Anomaly Transformer: Time series anomaly detection with association discrepancy, in: Proceedings of the Tenth International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.knosys.2026.116087_b7","series-title":"Proceedings of the Advances in Neural Information Processing Systems","first-page":"108231","article-title":"The elephant in the room: Towards a reliable time-series anomaly detection benchmark","volume":"Vol. 37","author":"Liu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116087_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113740","article-title":"Integrating local and global correlations with Mamba-transformer for multi-class anomaly detection","volume":"324","author":"Ma","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116087_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113124","article-title":"MGAN-LD: A sparse label propagation-based anomaly detection approach using multi-generative adversarial networks","volume":"312","author":"Li","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"9","key":"10.1016\/j.knosys.2026.116087_b10","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.14778\/3538598.3538602","article-title":"Anomaly detection in time series: A comprehensive evaluation","volume":"15","author":"Schmidl","year":"2022","journal-title":"Proc. VLDB Endow."},{"issue":"4","key":"10.1016\/j.knosys.2026.116087_b11","first-page":"308","article-title":"Time-series","volume":"25","author":"Anderson","year":"2018","journal-title":"J. R. Stat. Soc. Ser. D"},{"key":"10.1016\/j.knosys.2026.116087_b12","unstructured":"P. Malhotra, L. Vig, G. Shroff, P. Agarwal, Long short term memory networks for anomaly detection in time series, in: Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015."},{"key":"10.1016\/j.knosys.2026.116087_b13","series-title":"Proceedings of the Advances in Neural Information Processing Systems","article-title":"U-Time: A fully convolutional network for time series segmentation applied to sleep staging","volume":"Vol. 32","author":"Perslev","year":"2019"},{"key":"10.1016\/j.knosys.2026.116087_b14","series-title":"Proceedings of the 18th International Conference on Extending Database Technology","first-page":"481","article-title":"Time series anomaly discovery with grammar-based compression","author":"Senin","year":"2015"},{"key":"10.1016\/j.knosys.2026.116087_b15","series-title":"Proceedings of the Web Conference","first-page":"3124","article-title":"Time series change point detection with self-supervised contrastive predictive coding","author":"Deldari","year":"2021"},{"key":"10.1016\/j.knosys.2026.116087_b16","series-title":"Proceedings of the International Conference on Machine Learning","first-page":"4393","article-title":"Deep one-class classification","author":"Ruff","year":"2018"},{"key":"10.1016\/j.knosys.2026.116087_b17","unstructured":"L. Shen, Z. Li, J.T. Kwok, Timeseries anomaly detection using temporal hierarchical one-class network, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS \u201920, 2020."},{"key":"10.1016\/j.knosys.2026.116087_b18","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","first-page":"2733","article-title":"ITAD: Integrative tensor-based anomaly detection system for reducing false positives of satellite systems","author":"Shin","year":"2020"},{"issue":"1","key":"10.1016\/j.knosys.2026.116087_b19","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support vector data description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116087_b20","series-title":"Proceedings of the 2020 IEEE International Conference on Data Mining","first-page":"1118","article-title":"COPOD: copula-based outlier detection","author":"Li","year":"2020"},{"key":"10.1016\/j.knosys.2026.116087_b21","series-title":"Bayesian online changepoint detection","author":"Adams","year":"2007"},{"issue":"3","key":"10.1016\/j.knosys.2026.116087_b22","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/TAES.2017.2671247","article-title":"A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction","volume":"53","author":"Yairi","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"10.1016\/j.knosys.2026.116087_b23","unstructured":"B. Zong, Q. Song, M.R. Min, W. Cheng, C. Lumezanu, D.-k. Cho, H. Chen, Deep autoencoding Gaussian mixture model for unsupervised anomaly detection, in: Proceedings of the International Conference on Learning Representations, 2018."},{"key":"10.1016\/j.knosys.2026.116087_b24","series-title":"Proceedings of the 2008 Eighth IEEE International Conference on Data Mining","first-page":"413","article-title":"Isolation forest","author":"Liu","year":"2008"},{"key":"10.1016\/j.knosys.2026.116087_b25","series-title":"LSTM-based encoder-decoder for multi-sensor anomaly detection","author":"Malhotra","year":"2016"},{"key":"10.1016\/j.knosys.2026.116087_b26","series-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","first-page":"4433","article-title":"Beatgan: Anomalous rhythm detection using adversarially generated time series","author":"Zhou","year":"2019"},{"key":"10.1016\/j.knosys.2026.116087_b27","series-title":"Proceedings of the 2018 World Wide Web","first-page":"187","article-title":"Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications","author":"Xu","year":"2018"},{"key":"10.1016\/j.knosys.2026.116087_b28","series-title":"Proceedings of the 2023 IEEE International Conference on Multimedia and Expo","first-page":"2741","article-title":"A masked attention network with query sparsity measurement for time series anomaly detection","author":"Zhong","year":"2023"},{"key":"10.1016\/j.knosys.2026.116087_b29","series-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","article-title":"Sub-adjacent transformer: improving time series anomaly detection with reconstruction error from sub-adjacent neighborhoods","author":"Yue","year":"2024"},{"issue":"11","key":"10.1016\/j.knosys.2026.116087_b30","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.1007\/s10994-022-06153-4","article-title":"LatentOut: an unsupervised deep anomaly detection approach exploiting latent space distribution","volume":"112","author":"Angiulli","year":"2023","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116087_b31","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume":"Vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.knosys.2026.116087_b32","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"387","article-title":"Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding","author":"Hundman","year":"2018"},{"key":"10.1016\/j.knosys.2026.116087_b33","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2485","article-title":"Practical approach to asynchronous multivariate time series anomaly detection and localization","author":"Abdulaal","year":"2021"},{"key":"10.1016\/j.knosys.2026.116087_b34","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2828","article-title":"Robust anomaly detection for multivariate time series through stochastic recurrent neural network","author":"Su","year":"2019"},{"key":"10.1016\/j.knosys.2026.116087_b35","series-title":"Proceedings of the 2016 International Workshop on Cyber-Physical Systems for Smart Water Networks","first-page":"31","article-title":"SWaT: a water treatment testbed for research and training on ICS security","author":"Mathur","year":"2016"},{"key":"10.1016\/j.knosys.2026.116087_b36","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2123","article-title":"Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA","author":"Tariq","year":"2019"},{"issue":"2","key":"10.1016\/j.knosys.2026.116087_b37","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/335191.335388","article-title":"LOF: identifying density-based local outliers","volume":"29","author":"Breunig","year":"2000","journal-title":"SIGMOD Rec."},{"key":"10.1016\/j.knosys.2026.116087_b38","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","article-title":"A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder","volume":"3","author":"Park","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.knosys.2026.116087_b39","series-title":"Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"3220","article-title":"Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding","author":"Li","year":"2021"},{"key":"10.1016\/j.knosys.2026.116087_b40","series-title":"Proceedings of the 23rd International Conference on Machine Learning","first-page":"233","article-title":"The relationship between precision-recall and ROC curves","author":"Davis","year":"2006"},{"issue":"11","key":"10.1016\/j.knosys.2026.116087_b41","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.14778\/3551793.3551830","article-title":"Volume under the surface: A new accuracy evaluation measure for time-series anomaly detection","volume":"15","author":"Paparrizos","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.knosys.2026.116087_b42","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"635","article-title":"Local evaluation of time series anomaly detection algorithms","author":"Huet","year":"2022"},{"key":"10.1016\/j.knosys.2026.116087_b43","unstructured":"A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al., PyTorch: An imperative style, high-performance deep learning library, in: Proceedings of the Advances in Neural Information Processing Systems, Vol. 32, 2019."},{"key":"10.1016\/j.knosys.2026.116087_b44","series-title":"Proceedings of the 3rd International Conference on Learning Representations","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.knosys.2026.116087_b45","series-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks","article-title":"Revisiting time series outlier detection: Definitions and benchmarks","volume":"Vol. 1","author":"Lai","year":"2021"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008130?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008130?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:29:40Z","timestamp":1783124980000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":45,"alternative-id":["S0950705126008130"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116087","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SAformer: A time series anomaly detection model based on Similarity-Aware attention","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116087","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116087"}}